Remote-First Lead ML Inference Systems Engineer

Runpod; Mount Laurel Township (NJ); Remote; USD 150,000 - 220,000; Full time

Benefits offered by this job

Base salary

Equity

Medical/dental/vision

Flexible PTO

Remote-first culture

Home office stipend

Job summary

Runpod is hiring an ML Systems Engineer, Inference to own end-to-end LLM serving performance, optimizing latency and cost across models and hardware generations. This hands-on role requires identifying bottlenecks, implementing fixes, and delivering reliable production configurations.

You will profile serving stacks, define metrics, and collaborate with product and infra teams to shape Runpod's inference offerings. Remote-first team with competitive compensation and equity.

Qualifications

• 5+ years of professional system engineering experience.
• Deep, hands-on experience with vLLM, SGLang (or a comparable serving engine) in production or at serious benchmark scale.
• Strong software engineering skills in Python. You're comfortable working in large, performance-critical codebases.
• A solid understanding of what drives LLM inference performance: batching, memory, parallelism, and the trade-offs between latency and throughput.
• Experience with modern inference optimization techniques such as quantization, speculative decoding, or distributed serving.
• Rigor in benchmarking and performance analysis, plus comfort with GPU profiling tools.
• The ability to explain your results clearly in writing and turn them into decisions.
• CUDA or Triton kernel tuning is a plus.
• Experience with multi-node GPU systems and high-speed networking.
• Contributions to inference or ML systems projects.
• Experience at a company where inference cost and latency were core business metrics.

Responsibilities

• Define how we measure inference performance, including throughput, time to first token, inter-token latency, and cost per token, and build the tooling that makes those measurements rigorous and repeatable.
• Profile and diagnose performance problems across the serving stack, from scheduling and memory management down to kernels and interconnect.
• Improve serving efficiency for large, state-of-the-art models on single-node and multi-node GPU deployments.
• Turn what you learn into production-ready runtimes, configurations, and defaults that customers benefit from automatically.
• Work closely with product and infrastructure teams to shape how inference is offered on Runpod.
• Keep up with the fast-moving inference ecosystem, including the open-source community, and decide what's worth adopting, what's worth building, and what's worth contributing back.
• Trace bottlenecks in the serving engine/runtime and implement fixes when configuration tuning is not enough.

Skills

Python

Performance benchmarking

GPU profiling

System engineering

Tools

vLLM

SGLang

CUDA

Triton

Job description

Runpod is hiring an ML Systems Engineer, Inference to own end-to-end LLM serving performance, optimizing latency and cost across models and hardware generations. This hands-on role requires identifying bottlenecks, implementing fixes, and delivering reliable production configurations.

You will profile serving stacks, define metrics, and collaborate with product and infra teams to shape Runpod's inference offerings. Remote-first team with competitive compensation and equity.